Using chatbots to support student goal setting and social presence in fully online activities: learner engagement and perceptions
Open accessAbstract
Although fully online learning is now the 'new normal' in many parts of the world, its implementation is often beset by challenges such as the lack of student self-regulation, and the sense of isolation. In this paper, we explored the use of chatbots to support student goal setting (Study 1) and social presence (Study 2) in online activities. In Study 1, participants in a fully online course were invited to complete a goal setting activity prior to attending class via a goal-setting chatbot. The chatbot engaged participants with five questions developed based on the SMART (specific, measurable, achievable, realistic, and timely) goal setting framework. In Study 2, English-as-Foreign-Language participants in a fully online course were tasked to complete listening practices. The learning buddy chatbot was designed based on the social presence framework (interpersonal communication, open communication, cohesive communication) to guide students through listening exercises. In both Study 1 and 2, we evaluated participants' behavioral engagement by measuring their conversation records with the chatbots, as well as participants' perceived usefulness and ease of use of the chatbots. We also gathered in-depth interview data concerning the participants' perceptions of interacting with the chatbots. Overall, our findings found positive learner experiences with both chatbots with regard to the chatbots' perceived usefulness and perceived ease of use. We also provided suggestions for instructors to apply chatbots in teaching and learning.
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Undergraduate students’ learning outcomes with ChatGPT: A meta-analytic study
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- Authors
- Hew, K. F., Huang, W., Du, J., & Jia, C.
- Affiliations
- University of Hong Kong (HK)
- Zhejiang University (CN)
- Corresponding author
- Hew, Khe Foon
- Venue
- Journal of Computing in Higher Education
- Published
- 2022-09-09
- Volume
- 35
- Issue
- 1
- Pages
- 40-68
- ISSN
- 1042-1726 (ISSN-L)
- 1867-1233
- PMC
- PMC9458299
- OpenAlex
- W4295753326
- Cited by
- 245
- References
- 54
- Citations by year
- 2026: 53 · 2025: 89 · 2024: 59 · 2023: 43 · 2022: 1
- FWCI
- 15.80
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Computing in Higher Education (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- University of Hong Kong (Teaching Development Grant)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Online and Blended Learning
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- chatbots, student goal setting, social presence, fully online learning, student engagement, student self-regulation, learner isolation, SMART goals, English as a foreign language, listening practices, perceived usefulness, perceived ease of use, learner perceptions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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